European Journal of Radiology Open (Jan 2023)

Renal artery-based kidney segmentation on CT for patients with renal cell carcinoma: Feasibility of segmental artery clamping simulation

  • Kotaro Yoshida,
  • Atsushi Takamatsu,
  • Takahiro Nohara,
  • Norihide Yoneda,
  • Dai Inoue,
  • Wataru Koda,
  • Satoshi Kobayashi,
  • Toshifumi Gabata

Journal volume & issue
Vol. 10
p. 100463

Abstract

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Purpose: To evaluate the feasibility of renal artery-based segmentation of kidneys with renal cell carcinoma (RCC), based on three-dimensional (3D) software for the simulation of segmental artery clamping (SAC), and to correlate it with RENAL nephrometry score. Methods: Fifty RCCs (< 4 cm) identified from a pathological database search between January 2015 and January 2018 were included retrospectively. On computed tomography (CT) images, the relevant kidney, tumor, and renal artery were annotated semi-automatically on the commercial workstation, and renal artery-based segmentation was performed using 3D Voronoi diagrams. Simulation of SAC was performed by a radiologist and urologist in consensus. The volume of the whole kidney and tumor and estimated rescued volume for possible SAC cases were calculated. The correlation between possible SAC and RENAL nephrometry score was investigated. The reproducibility of the calculation of each volume and the interrater reliability of SAC simulation were assessed. Results: In the anatomical analysis, 44 patients had a single main renal artery and six had two main renal arteries, and of these, an early division pattern was observed in 11 cases. In the 3D simulation software, 22 out of 50 cases (44 %) were determined as possible SAC. The agreement of the SAC simulation was excellent (kappa = 0.96). RENAL nephrometry score was significantly different in the anterior/posterior and exophytic/endophytic components between possible and impossible SAC groups. Conclusions: Renal artery-based segmentation of kidneys with RCC on CT images using 3D simulation software is feasible for effectively estimating the possibility of SAC with high reproducibility.

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